Systems and Methods For Generating Issue Networks

ABSTRACT

Systems and methods for generating issue networks are disclosed. In one embodiment, a computer-implemented method of generating an issue network from a document corpus includes searching, using a computer, the document corpus for a set of documents discussing a starting issue, wherein the starting issue is one of a plurality of normalized issues defined by the document corpus. The method further includes determining a set of normalized issues discussed by the set of documents discussing the starting issue, wherein the set of normalized issues also includes the starting issue, and determining instances of co-occurrences of individual normalized issues of the set of normalized issues within individual cases of the set of documents. The method also includes linking individual normalized issues of the set of normalized issues based on their co-occurrences within the set of documents, wherein the linked individual normalized issues at least in part define the issue network.

BACKGROUND

1. Field

The present specification generally relates to methods for identifyingand organizing issues discussed within corpus of documents and, moreparticularly, to methods for extracting and organizing such issuesidentified in the document corpus into a structured issue network ofinterconnected normalized issues.

2. Technical Background

Documents within a corpus are often linked together by citations. Forexample, legal documents and scientific articles often cite to previousworks to support a particular rule, proposition or finding. In the legalcorpus context, an author of a judicial opinion often cites previouscases in support of his or her own legal statement or rule. In turn,these cited cases have themselves also cited and/or been cited by othercases in support of the proposition-in-question (and so on). Therefore,selected documents within the corpus are intrinsically linked togetheraround particular issues, and these links can be manifested in the formof citation networks.

Researchers often search the corpus for documents that discuss aparticular issue or topic. They will use the citations to move forwardand backward within the corpus to find additional relevant documents.However, documents, such as legal documents, may discuss many differenttopics or legal issues. Further, a document may cite a document for manydifferent reasons. Two citations pointing to the same document may citeto the same document for different reasons. Currently, the researcherdoes not know the particular issue or topic that a citing document isciting a cited document for based on the citation alone. The researchermust therefore sift through the many different cited documents. Further,issues may also be linked together by citation. A researcher may not beaware that particular issues are related. Because of this lack ofunderstanding of how particular issues are connected or otherwiserelated, the researcher may not perform a thorough and completeinvestigation into the original issue or research topic.

Accordingly, a need exists for alternative methods of extracting andorganizing normalized issues within a corpus of documents into an issuenetwork describing the interconnectedness of normalized issues withinthe corpus of documents.

SUMMARY

According to one embodiment, a computer-implemented method of generatingan issue network from a document corpus includes searching, using acomputer, the document corpus for a set of documents discussing astarting issue, wherein the starting issue is one of a plurality ofnormalized issues defined by the document corpus. The method furtherincludes determining a set of normalized issues discussed by the set ofdocuments discussing the starting issue, wherein the set of normalizedissues also includes the starting issue, and determining instances ofco-occurrences of individual normalized issues of the set of normalizedissues within individual cases of the set of documents. The method alsoincludes linking individual normalized issues of the set of normalizedissues based on their co-occurrences within the set of documents,wherein the linked individual normalized issues at least in part definethe issue network.

According to another embodiment, a computer-implemented system forgenerating an issue network from a document corpus, wherein documentswithin the document corpus are linked by citations, thereby forming acitation network, includes a processor and a non-transitorycomputer-readable medium storing computer readable instructions. Whenexecuted by the processor, the computer readable instructions cause theprocessor to search the document corpus for a set of documentsdiscussing a starting issue, wherein the starting issue is one of aplurality of normalized issues found within the document corpus,determine a set of normalized issues discussed by the set of documentsdiscussing the starting issue, wherein the set of normalized issues alsoincludes the starting issue, and determine co-occurrences of individualnormalized issues of the set of normalized issues within individualcases of the set of documents. The computer readable instructionsfurther cause the processor to link individual normalized issues of theset of normalized issues based on their co-occurrences within the set ofdocuments, wherein the linked individual normalized issues at least inpart define the issue network.

These and additional features provided by the embodiments describedherein will be more fully understood in view of the following detaileddescription, in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments set forth in the drawings are illustrative and exemplaryin nature and not intended to limit the subject matter defined by theclaims. The following detailed description of the illustrativeembodiments can be understood when read in conjunction with thefollowing drawings, wherein like structure is indicated with likereference numerals and in which:

FIG. 1 depicts a schematic illustration of a computing network for asystem for semantically pairing documents, according to embodimentsshown and described herein;

FIG. 2 depicts a schematic illustration of the server computing devicefrom FIG. 1, further illustrating hardware and software that may beutilized in performing the semantics-based citation pairingfunctionality, according to embodiments shown and described herein;

FIG. 3A depicts a schematic illustration of a document corpus accordingto one or more embodiments shown and described herein;

FIG. 3B depicts a schematic illustration of groups of documents havingsemantically-similar passages within a document corpus according to oneor more embodiments shown and described herein;

FIG. 3C depicts a schematic illustration of a group of documentsassociated with an issue and an issue library metadata entity accordingto one or more embodiments shown and described herein;

FIG. 4 depicts a flowchart illustration of a process for storinginformation regarding semantically-similar passages within documentsinto a plurality of issue library metadata entities;

FIG. 5 depicts a schematic illustration of a taxonomy structure of aplurality of issues within the document corpus according to one or moreembodiments shown and described herein;

FIG. 6 depicts a schematic illustration of a relationship between aciting document and a plurality of cited documents according to one ormore embodiments shown and described herein;

FIG. 7 depicts a schematic illustration of a document, acitation-pairing metadata file, a reason-for-citing metadata file, andrelationships therebetween according to one or more embodiments shownand described herein;

FIG. 8 depicts a flowchart illustration of a process for creating acitation-pairing metadata file according to one or more embodimentsshown and described herein;

FIG. 9 depicts a flowchart illustration of a process for semanticallypairing a reason-for-citing of a citing document with a cited-text-areaof a cited document; and

FIG. 10 depicts an exemplary graphical representation of an issuenetwork extracted from a document corpus.

DETAILED DESCRIPTION

Embodiments described herein are directed to systems and methods fororganizing issues discussed in a corpus of documents into an issuenetwork. Each document within the corpus may discuss one or more issues.Further, several individual documents within the corpus may discuss thesame issue. Although each of the passages discussing the issue may bephrased differently, they may be semantically similar and related to theparticular issue to which they discuss. There may be a large number ofissues discussed by the documents within the corpus. In many cases,individual issues are related in some way to other issues discussedwithin the corpus. For example, a first issue discussed within a casemay be commonly discussed in conjunction with a second issue in the samecase. Therefore, these two issues may be strongly related. Theco-occurrence of issues discussed in documents of the corpus may providean indicator as to the strength of the relationship between issues.

The issue networks described herein comprise a plurality of issuesextracted from the document corpus as interconnected nodes that areconnected to each other based on co-occurrence within documents. Theissue networks may provide a practitioner with a high-level view of howparticular issues are related to one another, and thereby provide him orher with a thorough understanding of the particular issue he or she isinterested in.

More specifically, embodiments utilize data-mining techniques to extractthe issues from the corpus and store the issues in a repository, such asan issue library. Such extracted issues stored within the issue librarymay be used as “tokens” that act as nodes within the issue network, asdescribed in detail below. The process by which issues are extracted,organized and stored is a data-driven and automatic process such thathuman intervention is minimal. In one embodiment, passages of individualdocuments are compared with other documents in the corpus to findsemantically-similar passages. These passages, which are referred toherein as issue instances, are then linked in a one-to-one relationshipand stored in a citation pairing metadata file. The citation pairingmetadata is then traversed to extract grouped issues by chaining thesame issue across all of the citation pairs. Information regarding thegroups of issues may be stored in individual issue library entries ascollections of issue instances. The issue library metadata entries maybe configured as individual issue library metadata files, a single largexml file containing the issue library metadata entities, or entriesstored in a database.

Metadata may be created and data-mined to generate connections betweennormalized issues. Such connections between normalized issues define anissue network, or a sub-network of a larger issue network. As describedabove, the co-occurrence of particular issues within a single case ordocument may indicate that there is a particular connection between theparticular issues. For example, a second issue may logically flow from afirst issue such that they are frequently discussed in an individualdocument, such as a legal case. Accordingly, the first and second issuesmay be related. The co-occurrences of normalized issues within thedocument corpus may be determined to define an issue network.Embodiments described herein utilize issues, such as legal issues, as aderived set of linguistic units derived from the document corpus as baseunits to model legal knowledge in a given legal system. The normalizedissues, being data-driven and semantics-specific, may be viewed as asummary, or a condensed version of knowledge, such as legal knowledge,and may support deeper analysis of the principles included in thedocument corpus. Various embodiments of methods and systems forgenerating issue networks of issues discussed in a document corpus aredescribed in greater detail herein.

As used herein, an “issue” (e.g., a legal issue) is a statement ofbelief, opinion, a principle, and the like. For example, in the legalcontext, an issue may be a rule of law. An issue usually contains one ormore concepts. As used herein, a “concept” is a building block of anissue. Below is an example statement defining a legal issue:

-   -   “Thirteen-year-olds should not own a vehicle.”

The above statement has at least three concepts: “thirteen-year-old,”“vehicle,” and “to own.” Further, the statement is providing an opinion,a belief or a law and is therefore a legal issue. Below are additionalexamples of legal issues extracted from legal documents of a corpus:

a) “An inference is not reasonable if it is based only on speculation.”b) “To constitute the crime of robbery, however, the use of force mustbe motivated by an intent to steal.”c) “ . . . a statute will not be given an interpretation in conflictwith its clear purpose, and that general words used therein will begiven a restricted meaning when reason and justice require it, ratherthan a literal meaning which would lead to an unjust and absurdconsequence.”d) “ . . . the initial question to be decided in all cases in which adefendant complains of prosecutorial misconduct for the first time onappeal is whether a timely objection and admonition would have curedthem.”

Concepts, on the other hand, are building blocks of discussion or issuesas used herein. The concept “vehicle,” for example, is used in all thefollowing legal issues:

a) “A police office may approach a stopped vehicle and inquire about anoccupant's well-being without intruding on the Fourth Amendment.”b) “In Nebraska, a vehicle can be a tool of the debtor's trade if thedebtor uses it in connection with or to commute to work.c) “State law governs the issue of security interests in motorvehicles.”d) “In Idaho, it is a felony to purport to sell or transfer a vehiclewithout delivering to the purchaser or transferee a certificate of titleduly assigned to the purchaser.”

As illustrated above, a “concept” may be used in discussion of different“issues.” “Issues,” in contract to “concepts” as used herein, are morespecific and may serve as stand-alone statements relevant to theauthor's discussion and argument. Accordingly, issues, such as legalissues, being full statements, can better represent the semantics ofdocuments. While concepts, topics and other linguistic units tell uswhat a discussion is generally about, issues tell us what the discussionis specifically saying.

At any given time, there is an unknown, finite number of issues beingdiscussed within a document corpus. These issues form the body ofknowledge of the document corpus. In the legal context, legal issuesform the body of knowledge of a legal system, and represent principlesof the law. Yet, for common law systems, this kind of knowledge is, to alarge extent, embedded in case documents in the form of free texts andtherefore undiscovered to a degree. This lack of comprehensivecompilation of all laws in the legal system (as opposed to codified lawsin continental legal traditions) imposes difficulties for legalprofessionals as well as information systems based on computers.

As described in detail below, embodiments of the present disclosure mayextract important issues from a case law corpus (or other corpus) andstore those issues in an issue library (e.g., a legal issue library).The building of the issue library relies on a data-mining process thatcollects issues in the corpus based on semantics-based networktraversing. This traverse function links citations related to a startingissue during a recursive search in the network space. The issues thatare found are then normalized and may be stored in the issue library.Embodiments also uncover the relationship between individual issuesthemselves, and form a network using issues as the base units of thenetwork. The issue network may disclose relationships between seeminglydisparate issues, which may provide an opportunity for a legalpractitioner to generate additional legal arguments.

Although the embodiments are described herein in the context of a corpusof legal documents, it should be understood that embodiments are notlimited thereto. For example, the systems and methods described hereinmay be utilized to create issue networks for legal documents, scientificresearch documents, news articles, journals, online data (e.g.,Wikipedia articles) and any other type of large corpus of documentswhere documents are linked by citations.

Referring now to the drawings, FIG. 1 depicts an exemplary computingnetwork, illustrating components for a system for generating issuelibraries and issue networks from documents within a corpus, accordingto embodiments shown and described herein. As illustrated in FIG. 1, acomputer network 10 may include a wide area network, such as theinternet, a local area network (LAN), a mobile communications network, apublic service telephone network (PSTN) and/or other network and may beconfigured to electronically connect a user computing device 12 a, aserver computing device 12 b, and an administrator computing device 12c.

The user computing device 12 a may be used to input one or moredocuments into an electronic document corpus as well as initiate thecreation of metadata, such as issue-library metadata and issues-by-casemetadata described below. The user computing device 12 c may also beutilized to perform other user functions. Additionally, included in FIG.1 is the administrator computing device 12 c. In the event that theserver computing device 12 b requires oversight, updating, orcorrection, the administrator computing device 12 c may be configured toprovide the desired oversight, updating, and/or correction.

It should be understood that while the user computing device 12 a andthe administrator computing device 12 c are depicted as personalcomputers and the server computing device 12 b is depicted as a server,these are nonlimiting examples. More specifically, in some embodimentsany type of computing device (e.g., mobile computing device, personalcomputer, server, etc.) may be utilized for any of these components.Additionally, while each of these computing devices is illustrated inFIG. 1 as a single piece of hardware, this is also merely an example.More specifically, each of the user computing device 12 a, servercomputing device 12 b, and administrator computing device 12 c mayrepresent a plurality of computers, servers, databases, etc.

FIG. 2 depicts the server computing device 12 b, from FIG. 1, furtherillustrating a system for generating issue libraries and networks and/ora non-transitory computer-readable medium for generating issue librariesand networks embodied as hardware, software, and/or firmware, accordingto embodiments shown and described herein. While in some embodiments,the server computing device 12 b may be configured as a general purposecomputer with the requisite hardware, software, and/or firmware, in someembodiments, that server computing device 12 b may be configured as aspecial purpose computer designed specifically for performing thefunctionality described herein.

As also illustrated in FIG. 2, the server computing device 12 b mayinclude a processor 30, input/output hardware 32, network interfacehardware 34, a data storage component 36 (which stores corpus data 38 a,citation-pairing metadata 38 b, reasons-for-citing metadata 38 c, andissue-library/network metadata 38 d), and a memory component 40. Thememory component 40 may be configured as volatile and/or nonvolatilememory and, as such, may include random access memory (including SRAM,DRAM, and/or other types of random access memory), flash memory,registers, compact discs (CD), digital versatile discs (DVD), and/orother types of storage components. Additionally, the memory component 40may be configured to store operating logic 42 and metadata logic 44(each of which may be embodied as a computer program (i.e., computerreadable instructions), firmware, or hardware, as an example). A localinterface 46 is also included in FIG. 2 and may be implemented as a busor other interface to facilitate communication among the components ofthe server computing device 12 b.

The processor 30 may include any processing component configured toreceive and execute computer readable instructions (such as from thedata storage component 36 and/or memory component 40). The input/outputhardware 32 may include a monitor, keyboard, mouse, printer, camera,microphone, speaker, and/or other device for receiving, sending, and/orpresenting data. The network interface hardware 34 may include any wiredor wireless networking hardware, such as a modem, LAN port, wirelessfidelity (Wi-Fi) card, WiMax card, mobile communications hardware,and/or other hardware for communicating with other networks and/ordevices.

It should be understood that the data storage component 36 may residelocal to and/or remote from the server computing device 12 b and may beconfigured to store one or more pieces of data for access by the servercomputing device 12 b and/or other components. As illustrated in FIG. 2,the data storage component 36 stores corpus data 38 a, which in at leastone embodiment, includes legal and/or other documents that have beenorganized and indexed for searching. The legal documents may includecase decisions, briefs, forms, treatises, etc. Other documents may alsobe stored, such as scientific documents. Similarly, citation-pairingmetadata 38 b generated by the metadata logic 44 a may be stored by thedata storage component 36 and may include information relating to thesemantically linked documents. Reasons-for-citing metadata 38 c may alsobe stored by the data storage component 36 and may include data relatedto the text excerpts corresponding citations present in documents of thecorpus. Issue-library/network metadata 38 d (e.g., issue-librarymetadata and issues-by-case metadata) may also be stored by the datastorage component 36 and may include data related to documents withinthe corpus that are organized by issue.

Included in the memory component 40 are the operating logic 42 and themetadata logic 44. The operating logic 42 may include an operatingsystem and/or other software for managing components of the servercomputing device 12 b. Similarly, the metadata logic 44 may reside inthe memory component 40 and may be configured to facilitate electronicgeneration of the citation-pairing, reasons-for-citing, issue-library,and issues-by-case metadata as described in detail below. The metadatalogic 44 may be configured to compile and/or organize metadata to enableadditional user applications, such as electronic document search andretrieval, organization of the documents within the corpus by issue, andgeneration of one or more networks of issues.

It should be understood that the components illustrated in FIG. 2 aremerely exemplary and are not intended to limit the scope of thisdisclosure. More specifically, while the components in FIG. 2 areillustrated as residing within the server computing device 12 b, this isa nonlimiting example. In some embodiments, one or more of thecomponents may reside external to the server computing device 12 b.Similarly, while FIG. 2 is directed to the server computing device 12 b,other components such as the user computing device 12 a and theadministrator computing device 12 b may include similar hardware,software, and/or firmware.

Referring initially to FIG. 3A, a corpus 100 of documents isillustrated. Within the corpus 100 are individual documents 103 that arelinked via citations. For example, a citing document may include acitation that references a particular passage or text area of a citeddocument. The cited document may further cite other documents and so on.The citations and linked documents form a citation network within thecorpus 100. It should be understood that the corpus 100 may include anynumber of documents 103.

The corpus 100 of documents may be a legal corpus comprising manyindividual judicial opinions. In some common-law countries, such as theUnited States, the legal system is based on stare decisis, whereinjudges are obligated to follow the precedents established by priorjudicial decisions. When preparing a judicial or legal opinion, thejudge or person preparing the opinion must cite to documents to supportparticular rules, statements and facts. A citation is commonly locatedproximate to a reason-for-citing, i.e., the string of text that islocated near the citation and suggests the reason for the particularcitation to the cited document. Legal research is often performed bysearching prior and subsequent cases of a legal issue based on citationslocated within each document. Therefore, knowing and understanding thereasoning why cases are linked together within the corpus 100 may bebeneficial for efficient legal research.

Referring now to FIGS. 3B and 3C, an example of a plurality ofsemantics-based sub-networks 105 a-c of issue instances discussed incases that are each relevant to a particular issue or sub-issue existswithin the corpus 100 is illustrated. Issue instances are passageswithin the individual documents of the corpus 100 that discuss issues.Although issue instances may be phrased differently, they may have thesame semantic meaning. For example, there may be many ways to describe aparticular rule of law; however, each description of the particular ruleof law, although different, may be semantically the same. Issueinstances cited within the documents of a corpus that discuss aparticular issue form a sub-network that is defined as a collection ofissue instances that discuss the particular issue.

FIG. 3C illustrates a plurality of documents (each numbered 103) thatcite and/or are cited for a particular issue. The issue instances withinthe documents schematically illustrated in sub-network 105 a are relatedto one particular issue, the issue instances within the documentsschematically illustrated in sub-network 105 b are related to anotherissue, and the issue instances within the documents schematicallyillustrated in sub-network 105 c are related to yet another issue. Forexample, the individual issue instances present within a particularsub-network may be related to the issue as to when it is appropriate fornew arguments to be introduced on appeal. These issue instances may forma collection of issue instances relevant to the issue as to when it isappropriate for new arguments to be introduced on appeal.

Many issues within the corpus have semantic relationships amongstthemselves, or interact with each other semantically. As described inmore detail below, data-mining and semantics-based traversing are usedto discover these sub-networks and organize them into issues that forman issue library. Embodiments determine how such issues within the issuelibrary are connected and related amongst themselves to define an issuenetwork. The issue network uses the issues themselves as interconnectednodes within a network based on their co-occurrences within cases of thecorpus 100,

FIG. 3C illustrates a sub-network 105 c of cases that has been extractedfrom the corpus 100. The sub-network 105 c is composed of a plurality ofdocuments 103 a-j that each has at least one passage that issemantically similar to a particular issue (i.e., an issue instance).Information regarding the extracted issue, the document citations, andsemantically-similar passages may then be written and stored into anelectronic issue library metadata entity 105. The issue library iscomposed of issue library metadata entities that are generated from thecorpus. In one embodiment, the issue library metadata entries may beconfigured as individual issue library metadata files. Alternatively,all of the issue library metadata entries may be stored together in alarge xml file or a database.

The issue library metadata entity contains the issue-related informationthat may be utilized by software programs to perform various functions.As described in detail below, the issue library metadata entities maycomprise an issue text statement that states the particular issue,citation information regarding the cases that discuss the issue, andissue instances of the discussion of the particular issue. The issueinstances (i.e., the text passages within the documents that discuss theparticular issue) are identified and represented in the issue library bya unique identification number and a standard issue text statement.Therefore, the issue library metadata provides normalization to theissues that are extracted from the corpus by associating individualissue instances with an issue having an identification number.Normalization of the many issues discussed within the document corpusallows the issues to be searched and organized into a network. The manyinstances of the particular issue may be normalized in a single unit ofthe issue library metadata. Those units or entries may then be utilizedfor further processing.

Other metadata may also be created. For example, the issues-by-casemetadata described in detail below includes normalized issue vectors forall or some of the cases within the corpus. More specifically, theissues-by-case metadata may include information regarding the variousnormalized issues discussed by each document in the corpus. The issuelibrary metadata and the issues-by-case metadata may be storedseparately from the documents of the corpus.

As described in more detail below, normalized issues extracted from thecorpus may be used as units in a network to depict the relationshipsbetween various issues. Issues, such as legal issues in the legalcontext, are connected by citations and other semantic elements. Whenthey are used as the basic units to form networks, much of the legalknowledge that has not been easily seen may be revealed.

The corpus may be data-mined to discover various issues that arediscussed within the corpus. Use of the data-mining techniques describedherein greatly enhances the ability to organize the corpus (which mayconsist of millions of documents) by various issues. Embodiments may beused to develop an issue library for an existing corpus as well ascontinuously and automatically add documents into the relevant issuelibrary metadata entities as they are added to the corpus. For example,in a legal corpus, judicial opinion documents that are issued by a courtmay be analyzed as described herein and then respective passages may beadded to the proper issue library metadata entities. Embodimentsdescribed herein also take normalized issues extracted from a documentcorpus and extract one or more issue networks based on theinterconnectedness of the normalized issues within the corpus. Forexample, a first issue within a first document may be discussedextensively in conjunction with a second issue in a large number ofother documents in the corpus. Therefore, the connection between thefirst issue and the second issue may form a branch within an issuenetwork or sub-network of an issue network. Embodiments described hereinextract an issue network (and/or issue sub-networks) from the corpus toreveal connections between issues that may otherwise not be apparent.

Described below is an exemplary method for extracting issues from acorpus, normalizing the extracted issues, and then generating an issuelibrary using various metadata. Next, an exemplary method of generatingan issue network from extracted and normalized issues is described.

FIG. 4 is a flowchart 120 that illustrates an exemplary data preparationprocess for extracting issues from the corpus and storing issue-relatedinformation into individual issue library metadata entities. At block121, one or more documents of the corpus are entered into a computersystem. At block 122, passages within individual documents are linkedwith semantically-similar passages of cited documents. Each documentwithin the corpus may comprise one or more citations that link theciting document to one or more cited documents. A reason-for-citing iscommonly present near the citation and suggests the particular reasonfor the citation. The reason-for-citing is often related to a particularissue of discussion. The citation within the citing document commonlyrefers to a reason-for-citing or a cited-text-area in a cited document.

A cited-text-area within a cited document may or may not have anassociated citation. For example, a drafter of a judicial opinion maycite to a previous judicial opinion that is the originator of aparticular rule the drafter wishes to incorporate into his or herjudicial opinion. The rule in the previous judicial opinion most likelydoes not contain a citation. However, it is common for citing documentsto cite previous reasons-for-citing in cited documents that haveassociated citations. Therefore, it is to be understood that areason-for-citing is a passage of text that has an associated citationand a cited-text-area is a passage of text that does not have anassociated citation. Reasons-for-citing and cited-text-areas in a citeddocument may be treated as equivalents according to the embodimentsdescribed herein.

As described in more detail below with respect to FIGS. 6-9, links areformed between passages of a citing document and thesemantically-similar passages of the cited document(s) that it cites,thereby generating one-to-one relationships between the passages. In oneembodiment, the passages that are linked semantically are thereasons-for-citing and cited-text-areas of the citing and citeddocument. For example, a link may be formed between a reason-for-citingof the citing document with the reason-for-citing or cited-text-area ofa cited document that is most semantically similar to thereason-for-citing of the citing document. These semantic links may begenerated as described below for each (or substantially each)reason-for-citing within the documents of the corpus.

At block 124, information regarding the links for the documents in thecorpus are stored as individual citation entries in a citation-pairingmetadata file. The citation-pairing metadata file contains one-to-onepairing information between a reason-for-citing of a citing document anda reason-for-citing/cited-text-area of a cited document. As an exampleand not a limitation, the citation-pairing metadata file may containinformation such as a citing document identifier, a reason-for-citing, acited document identifier, and a cited case reason-for-citing orcited-text-area. The citation-pairing metadata file may provide a singlerepository for the linked passages that may be easily accessed andutilized by various applications. Examples of citation-pairing metadatafiles and corresponding citation entries are described below withrespect to FIG. 7.

The citation-pairing metadata file may be used to extract issues fromthe corpus. At block 126, the citation network of the corpus istraversed by searching the citation-pairing metadata file for passagesthat are semantically similar to one another. Semantically-similarpassages that discuss the same issue are grouped together to form asub-network of the citation network (e.g., sub-networks 105 a-billustrated in FIG. 3B). Text strings associated with thesemantically-similar passages are retrieved based on their citationlinks. A depth-first search of the citation-pairing metadata file isperformed to search all nodes (i.e., a discussion of an issue within adocument) of the citation network that discuss the same or similarissue.

One exemplary method of traversing the citation network to determinedocuments having semantically-similar passages is described in U.S. Pat.No. 7,735,010, which is incorporated herein by reference as though fullyset forth in its entirety. Generally, the exemplary method comprisesperforming a depth-first search of the semantic links in thecitation-pairing metadata file based on either a user-specified issuerepresented by a reason-for-citing or a headnote, or anautomatically-generated issue. A headnote is text that summarizes anissue found in a document and is expressed in the actual language usedin the document. To extract entries for the issue library, anautomatically-generated issue may be determined by systematically orrandomly selecting a reason-for-citing in a citing or cited document andsearching for passages in documents that are semantically similar tothat selected reason-for-citing. At each node a list of newreasons-for-citing candidates or headnotes is returned, and each ofthese new reasons-for-citing or headnotes is used to search for morecandidates in a similar manner. The retrieved citations andcorresponding semantically-similar passages are used to form thesub-network and are grouped together to be included under thecorresponding metadata entries for the issue library.

Information regarding the groups of semantically-similar passages anddocuments may be stored in a plurality of issue library metadataentities at block 128. In one embodiment, each issue library metadataentity may be associated with one particular issue. Alternatively,multiple issues may be stored in a single issue library metadata entity.For example, groups of related issues may be stored in one issue librarymetadata entity. The process illustrated in FIG. 4 may be repeatedlyexecuted to exhaustively mine the corpus to extract issues, grouppassages and documents by issue, and store such passages and documentsin issue library metadata entities. The process may also be performedeach time a new document is added to the corpus to extract the issuesthat the document discusses and place such issues in the appropriateissue library metadata entity.

The above-described process allows cases to be grouped under the sameissue library metadata entity and therefore the same issue identifiereven when the language of the discussion is varied. The followingexcerpts (i.e., issue instances) from different cases show this kind ofvariation:

a) “Robbery is ‘the felonious taking of personal property in thepossession of another, from his person or immediate presence, andagainst his will, accomplished by means of force or fear.’ The intent tosteal must be formed either before or during the commission of the actof force.”b) “According to Green, under California law, the crime of robberycannot be committed if the intent to steal is formed after the murder.”c) “Defendant testified that he had not thought about stealing any ofMullins' property until after the assault was completed. If defendanthad not harbored a larcenous intent before or during the assault, thetaking was theft rather than robbery.”d) “No robbery occurs when the intent to steal is formed after the useof force.”e) “Defendant claims his various admissions go to the killing and notthe robbery. Further, he argues there was no evidence showing he formedthe intent to rob before he killed the victim.”f) “Defendant's claim of insufficient evidence is premised on amisunderstanding of the immediate presence element of robbery. So longas defendant formed the intent to take the Brandts' possessions beforekilling them, he was properly convicted of robbery.”

Despite the variation in linguistic expression, these passagesrepresenting issue instances are clearly statements of the same legalissue regarding the nature of the intent required to support a charge ofrobbery, and may be duly stored within a library metadata entry. In thisway, instances of the same issue are normalized, and collapsed into thesame issue identifier with or without links to their original cases.This allows cases or documents to be grouped under the same issueidentifier within a library metadata entry even when the langue ofdiscussion is varied. In the legal context, each legal issue thusextracted may be considered a small piece of law in the particular legalsystem. The collection of all issues may be seen as a summary orcondensed version of legal knowledge of the legal system.

The format and contents of the issue library metadata entities may beconfigured in a variety of formats. One example of an issue librarymetadata entity is provided below in Table 1 below. It should beunderstood that the exemplary issue library metadata entity below is forillustrative purposes only and that embodiments may have more or fewerentries, as well as different types of entries. Although the issuelibrary metadata entities may be constructed in a table, a table isbeing used herein for ease of illustration and discussion and not as alimitation.

TABLE 1 Metadata Field Exemplary Metadata Field Entry Issue Identifier:I-000001 Display Issue Text: “It is well settled that rescission cannotbe effected without an offer to restore, the only exception to this rulebeing where the vendee has received nothing of value. Index Terms:restore, rescission, . . . Taxonomy Topic: “Rescission & Redhibition”Issue Instance 1: “He must give prompt notice of his election to rescindthe contract, and he must restore or offer to restore everything ofvalue which he has received thereunder.” (Taylor v. Hammel, 39 Cal. App.205) Issue Instance 2: “Exceptions to the general rule that one seekingrescission in equity must as a condition precedent to action promptlyrescind and restore or offer to restore what he has received are casesin which by reason of special circumstances it has on general equitableprinciples become unfair to impose such a condition of relief.” (Walshv. Majors, 4 Cal. 2d 384) Issue Instance 3: “. . . there can be norescission of an executed contract, upon the ground of fraudulentmisrepresentation, without restoration before suit by the party seekingto rescind of everything of value which he had received from the otherparty under the contract, or a bona fide offer to restore.” (Kelley v.Owens, 120 Cal. 502 Issue Instance 4: . . .

Referring to Table 1, the issue identifier field points to a particularissue that has been extracted from the corpus as described above. Theissue identifier may be a unique code that corresponds to the particularissue. Each issue may be assigned a unique issue identifier. The issueidentifier “I-000001” indicates an issue discussed within the corpus. Itshould be understood that embodiments described herein are not limitedto the issue identifier format illustrated in Table 1. For example, theissue identifier may be a numeric code, an alphabetic code, or analphanumeric code. Any number of formats may be utilized for the issueidentifier.

The display issue text field contains a string of text that isassociated with the particular issue of the issue identifier. In oneembodiment, the display issue text string is an actual string of textfrom a document in the corpus that best represents the particular issue.The display issue text may be selected from all of the text strings(e.g., reasons-for-citing and cited-text-areas) of the documents thatdiscuss the particular issue. These text strings are referred to asissue instances. As an example and not a limitation, one hundred casesmay discuss a particular issue and be grouped together. The text stringsthat discuss the issue may be evaluated such that a single text stringis selected from the one hundred issue instances in the group that bestrepresents the particular issue. The selected text is designated as thedisplay issue text and stored in the display issue text field of theissue library metadata entity. The display issue text may be the textthat is presented to an end-user to provide a summary of the particularissue, for example. The display issue text may be selected automaticallybased on linguistic and other rules. For example, the issue instancesmay be evaluated and scored based on the number of key terms within thetext string, the length of the text string, the date of document, etc.The display issue text may be selected in other manners as well, such asmanually by a person.

The index terms field contains key terms that are relevant to theparticular issue. The index terms may be generated automatically bycomparing the text strings of the issue instances with a list of keyterms and extracting those terms that are frequently contained in thetext strings. The index terms may also be entered manually by a personwho evaluates the issues and determines which terms are to be used asthe index terms.

In some embodiments, the particular issues of the corpus may be placedwithin a taxonomy structure that organizes the corpus. The taxonomyspecifies hierarchically-structured topics. The taxonomy may beorganized by a tree of taxonomy topics. Each topic in the taxonomy maybe seen as a place to host one or more issues. FIG. 5 illustrates ataxonomy structure that comprises three topics: taxonomy topic 112,taxonomy sub-topic one 114, and taxonomy sub-topic two 116. It should beunderstood that more or fewer taxonomy topics may be utilized dependingon the concepts discussed in the corpus. As an example and not alimitation, taxonomy topic 112 may be titled “Contracts,” taxonomysub-topic one may be titled “Remedies,” and taxonomy sub-topic two maybe titled “Redhibition” in a legal corpus. It should be understood thatany other taxonomy topics and sub-topics may be present within thetaxonomy structure.

The issue instance fields contain information relating to the instanceswhere the particular issue is discussed in the documents. The issueinstances are text strings of reasons-for-citing and/or cited-text-areasthat are related to the particular issue. In one embodiment, as depictedin Table 1, the issue instance fields may be populated with the actualtext of the issue instance in the documents. The issue instance fields,in a legal context, may therefore contain the text of the cited rules aswritten in the documents. In another embodiment, the issue instancefields may contain an issue instance identifier that points to an entryin another metadata file that contains the actual text of thereason-for-citing or cited-text-area. As an example and not alimitation, a reason-for-citing metadata file may be used to store thetext associated with reasons-for-citing within documents of the corpus.One embodiment of a reason-for-citing metadata file is described belowwith respect to FIG. 7.

The issue instance fields may also contain a link to the actual documentthat the issue instance is related to. The issue instance may beaccessed by an end-user or a software program to retrieve the documentthat the particular issue instance is from. In one embodiment, theactual citation may be included in the issue instance field. In anotherembodiment, a document identifier may be provided that points to thelocation of the actual document for retrieval.

The issue library metadata entity may also contain additionalinformation that is not depicted in Table 1. For example, the issuelibrary metadata entity may contain information regarding a citedstatute or statutes that are related to the particular issue, as well asa cited article or articles, such as law review articles for example,that discuss the particular issue. Links to the most frequently citeddocuments for the particular issue may also be included in the librarymetadata file, as well as documents that are held in high regard by acommunity and, in the legal context, Shepard's treatment informationregarding the particular issue.

In this manner, one or more issues may be extracted from the corpus andthen normalized as a library metadata entry.

Referring once again to FIG. 5, in some embodiments, the variousextracted issues may be organized under a taxonomy structure 110 thatdefines an issue library. FIG. 5 illustrates one example of a portion ofsuch a taxonomy structure. The nodes 117 positioned under taxonomysub-topic two 116 represent various issues extracted from the corpus.These issues are relevant to the particular taxonomy topic andsub-topics—that are depicted. Using the example from above, the issuesrepresented by nodes 117 may be related to ContractsLaw→Remedies→Rescission & Redhibition. Each node has a unique issueidentifier associated therewith. It should be understood that theillustrated issue identifiers of FIG. 5 are for demonstrative purposesonly. Referring to the example of Table 1 above, issue I-000001 isdirected toward rescission and may therefore be placed under thetaxonomy topic or sub-topic “Rescission & Redhibition.”

A plurality of issue instances 118 are positioned under the nodes 117representing the issue instances. Each box under an issue identifier mayrepresent one or more issue instances, as illustrated in Table 1. Forexample, some issues may have as many as thousands or tens of thousandsof associated issue instances. Other issues may only have a fewassociated issue instances.

An exemplary process for the generation of the citation-pairing metadatafile referenced above as well as its operation will now be describedhereinbelow. The citation-pairing metadata file assists in the creationof the issue library metadata entities described above.

FIG. 6 illustrates a citing document 101 and a plurality of citeddocuments 104 a-d. The illustrated citing document 101 has fourcitations and corresponding reasons-for-citing 102 a-d. Eachreason-for-citing 102 a-d is located proximate to a citation within thecited document 101. The citations link the citing document 101 to theplurality of cited documents 104 a-d. The drafter of the citing document101 has a particular reason for citing each cited document 104. Forexample, the drafter of the citing document may wish to incorporate aparticular rule from cited document 104 a into the cited document. Thecited-text-area 106 a of cited document 104 a may recite the particularrule that corresponds with the reason-for-citing 102 a of the citingdocument 101. The reason-for-citing 102 a and cited-text-area 106 a maybe semantically similar. As illustrated, the citing document 101 andcited document 104 a are linked at both a document level and a passagelevel. Similarly, reason-for-citing 102 b is semantically linked tocited-text-area 106 b of cited document 104 b, reason-for-citing 102 cis semantically linked to cited-text-area 106 c of cited document 104 c,and reason-for-citing 102 d is semantically linked to cited-text-area106 d of cited document 104 d.

However, the citations only identify the particular cited documentscited by a citing document, and not the particular text area or passagethat is being cited. Current pairing techniques are asymmetric because areason-for-citing is at the citing document end of the link, but at theother end it is the whole case: Case_X:Reason_For_Citing_a→Case_Y.Embodiments described herein enable cases to be linked at the passagelevel on both ends of the link and store citation entries within acitation-pairing metadata file that contains information regarding thesemantically linked pairing.

The citation-pairing metadata file specifies the citation relationshipbetween two cases at the semantic level (i.e., at the passage level).The citation-pairing metadata file contains a citation entry for eachreason-for-citing of every document within the corpus (or a select groupof documents within the corpus). Below is an example of a citation entryformat of one embodiment:

CitingCaseID:Reason-For-CitingID::CitedCaseID:CitedTextAreaID::SimilarityValue

The CitingCaseID and CitedCaseID fields of the above example are aciting document identifier and a cited document identifier,respectively. These identifiers contain information that point toparticular documents within the corpus. Within each citing document area plurality of reasons-for-citing or rules. For example and notlimitation, the citing document may have 20 citations and therefore 20corresponding reasons-for-citing. The Reason-For-CitingID field is areason-for-citing identifier that points to the particularreason-for-citing within the citing document. For example, thereason-for-citing identifier may point to the fifth reason-for-citing inthe citing document, which may be for a particular rule of law.

A plurality of reasons-for-citing or rules are also present within eachcited document. If the document is a legal document and the citeddocument is cited for a legal issue, there is usually a text area in thedocument that discusses the legal issue, and in most cases, thecited-text-area is located near another citation referencing anotherdocument. Therefore, there is a high likelihood that thereason-for-citing in the citing document is referring to acited-text-area that corresponds to a reason-for-citing in the citeddocument. The CitedTextAreaID field is a cited-text-area identifier andcommonly points to a reason-for-citing in the cited document.

The value present in the SimilarityValue field represents the relativesemantic similarity between the text associated with theReason-For-CitingID and the text associated with the CitedTextAreaID.The SimilarityValue will be described in more detail below.

An example of a citation entry included in a citation-pairing metadatafile is provided below. It should be understood that the format andcontent of the exemplary citation entry may vary and embodiments are notlimited thereto.

A72D7FE70BE40038:R_(—)1::A26169830BE40246:R_(—)5::0.832590108

In the above example, “A72D7FE70BE40038” is the citing-documentidentifier and may point to the case Rolley, Inc. v. Merle NormanCosmetics, Inc., 129 Cal. App. 2d 844, for example. R_(—)1 is thereason-for-citing identifier and corresponds to the firstreason-for-citing in the citing case. As described in more detail below,the Reason-For-CitingID may point to an entry in a separatereason-for-citing metadata file. In the above example, R_(—)1 of citingdocument Rolley, Inc. may state that:

-   -   “Appellate courts cannot submit to piecemeal argument and will        not consider on petition for rehearing questions not previously        raised.”

CitedCaseID A26169830BE40246 may point to the cited case Bradley v.Bradley, 94 Cal. App. 2d 310, for example. The CitedTextID of R_(—)5indicates that the cited-text-area of the cited case is the fifthreason-for-citing. R_(—)5 may point to an entry in a reason-for-citingmetadata file that the fifth reason-for-citing in Bradley states:

-   -   “The case having been tried on the theory that condonation was        not an issue appellant under settled principles cannot now        change his theory [***3] appeal to the disadvantage of        respondent.”

Therefore, the above exemplary citation entry states that “Rolley, Inc.v. Merle Norman Cosmetics, Inc.” cited “Bradley v. Bradley” for thelegal issue of the ability for a party to raise new issues on appealwith a similarity measure between the two reasons-for-citing of about0.8.

Referring now to FIG. 7, a schematic illustration of a document 144, acitation-pairing metadata file 130 and a reason-for-citing metadata file140 are illustrated. The document 144, citation-pairing metadata file130 and reason-for-citing metadata file 140 are stored separately fromone another. The citation-pairing metadata file 130 comprises aplurality of citation entries (e.g., citation entry 131). Depending onthe size of the corpus, the citation-pairing metadata file 130 may havehundreds of thousands of citation entries. Each citation entry hassemantic-pairing information associated therewith. The citation-pairingmetadata file 130 may be accessed by a computer system to obtaininformation regarding passages relevant to particular issues or topics,or to find documents that discuss particular issues. As illustrated inFIG. 7, CASE_Y may contain linking information that, when accessed by anend-user and/or a computerized system, may retrieve the actual text ofthe document 144 corresponding to the CASE_Y CitedCaseID 134. Forexample, a user may initiate query using a software program configuredto access the citation-pairing metadata file 130 to retrieve cases thatcite a particular reason-for-citing.

The reason-for-citing metadata file 140 includes many reason-for-citingentries (e.g., reason-for-citing entry 143). The purpose of thereason-for-citing metadata file 140 is to provide the actual text stringof reasons-for-citing associated with the documents in the corpus. Asillustrated in FIG. 7, each reason-for-citing entry within thereason-for-citing metadata file 140 has information related toreasons-for-citing associated with each document in the corpus. In oneembodiment, the reason-for-citing entry may have the following format:

-   -   CaseID:Reason-For-CitingID:Text_of_Reason-for-Citing

The CaseID may be the same document identifier described above, whereinthe document identifier points to or is otherwise associated with aparticular document in the corpus. The Reason-For-CitingID may be asdescribed above and points to the particular reason-for-citing withinthe associated document. The Text_of_Reason-for-Citing contains theactual text string of the reason-for-citing (or cited-text-area) withinthe document. As shown in FIG. 7, each case may contain a plurality ofreasons-for-citing/cited-text-areas. For example, “CASE_Y” has sixreasons-for-citing. In one embodiment, all of the documents of thecorpus are stored in a single reason-for-citing metadata file.Alternatively, more than one reason-for-citing metadata file may beused. In one embodiment, each document may have its ownreason-for-citing metadata file.

The reason-for-citing metadata file 140 may be accessed via thecitation-pairing metadata file 130 to obtain the text strings associatedwith reasons-for-citing and cited-text-areas within documents. In thismanner, the citation-pairing metadata file 130 may be smaller in sizebecause the text strings of each reason-for-citing/cited-text-area arenot stored in the citation-pairing metadata file 130 but rather in thereason-for-citing metadata file.

Referring to FIG. 7 as an example, reason-for-citing/cited-text-area“R_(—)5” of “CASE_Y” of the citation-pairing metadata file 130 (e.g.,identifiers 134 and 132 of citation entry 131) may point toreason-for-citing entry 143 of the reason-for-citing metadata file 140.Reason-for-citing entry 143 is directed to the fifth reason-for-citing(“R_(—)5”) of the document CASE_Y. Reason-for-citing entry 143 alsocontains the text string of the reason-for-citing.

The citation-pairing metadata file and reason-for-citing metadata fileenable the storage of voluminous amounts of data relating to documents,citations, related text passages and links in a relatively compact andeasily-accessed format. The metadata is configured in such a way thatallows for quick access and linking to support various software programsand applications, such as searching applications (e.g., more-like-thissearching programs), issue libraries (i.e., groups of documents and/orissues/topics), and support of a citation network viewer in which theend-user may graphically view the citation network and sub-networks.

Software programs and applications may use the citation-pairing metadatafile 130 and reason-for-citing metadata file 140 as described above toprovide an end-user with the reasons-for-citing for the particularissues/topics he or she may be interested in. The end-user may perform a“more-like-this” search in which the software program accessesadditional documents and reasons-for-citing related to the particularissue at hand.

Using the embodiments described herein, documents may be linked togetherbeyond simple citation patterns alone or text matching alone. Themetadata described herein can be used to link passages from differentdocuments discussing the same topic/issue. It may give researches theability to search document citations based on topics as well ascitation. Embodiments may improve any search when an end-user ispresented with a passage and hopes to find additional documentsresembling the passage. Software programs using the embodimentsdescribed herein may proactively choose passages behind the scenes(using the citation-pairing metadata and reason-for-citing metadata)that are relevant to an end-user's search activities even whendissimilar language is used.

The creation of the pairing information and data that is populated intothe citation-pairing metadata file will now be described. FIG. 8illustrates a flowchart 150 that describes the process of creating thecitation-pairing metadata file by populating the file withcitation-pairing entries. The text of documents with a document corpusis input into a computer system at block 151. The computer system hascomputer code stored thereon that is operable to perform the variousfunctions described herein. The corpus may be a legal corpus of aparticular court or group of courts. For example, the legal corpus maybe the all federal courts of appeals and the documents may be alljudicial opinions (cases) associated with the federal courts of appeal.The corpus may also be a single court, such as the Court of Appeals forthe Federal Circuit or the California Court of Appeal, for example. Thelegal corpus may also be an entire universe of legal opinions that spanall state, federal and local courts.

At block 152, a reason-for-citing is determined for each citation withinthe document. The reasons-for-citing may be determined via the use of areason-for-citing algorithm that is configured for identifying text in aciting court case near a citation (i.e., a citing instance), whichindicates the reason(s) for citing. The reason-for-citing algorithm aidsin the development of the citation-pairing metadata file by correctlylocating reason-for-citing and cited-text-areas, as well as theirrespective boundaries within the document. One embodiment of areason-for-citing algorithm is described in U.S. Pat. No. 6,856,988,which is incorporated herein by reference as though fully set forth inits entirety. Generally, the reason-for-citing algorithm includes thesteps of: obtaining contexts of the citations (i.e., citing instances)in the citing document (each context including text that includes thecitation and the text that is near the citation), analyzing the contentof the contexts, and selecting (from the citing instances' context) textthat constitutes the reason-for-citing, based on the analyzed content ofthe contexts. The boundaries of the determined reasons-for-citing may bemarked within the text of the document. For example, the boundaries maybe marked with XML tags that delineate the text of thereasons-for-citing from the remaining text of the document. Subsequentprocesses, such as the processes described below, may use the XML tagsor other markers to determine the locations of the variousreasons-for-citing.

At block 154, the text area of a cited document that the citing documentis citing is located. This step finds the text area in the citeddocument that is most semantically-equivalent to the reason-for-citingin the citing document. One method of determining the cited-text-areathat is most semantically-equivalent to a reason-for-citing is describedin U.S. Pat. No. 7,735,010. Generally, referring to the flowchart 160 ofFIG. 9, the reasons-for-citing are determined in the cited document withthe reasons-for-citing algorithm described above. The reasons-for-citingwithin the citing and cited documents are turned into vectors (e.g., bythe use of key term extraction, lexical normalization, weighing, etc.).The vectors of the citing document and cited documents are paired andsemantically compared with one another at block 162. A similarity valueis established for each reason-for-citing within the cited document(s)at block 164. A vector comparison function may be used to measure thesimilarity between the two vectors. If there are remainingreasons-for-citing in the cited document(s) at block 166, the nextreason-for-citing in a cited document is selected at block 168 and theprocess is repeated at block 162. If there are no more remainingreasons-for-citing at block 166, the reason-for-citing of a particularcited document having the highest similarity value is selected as thecited-text-area at block 169.

Referring once again to FIG. 8, after the cited-text-areas of the citeddocuments are determined at block 154, a citation entry is written foreach reason-for-citing of the citing document into the citation-pairingmetadata file at block 155. As described above, a citation entrycontains information related to the citing document, thereason-for-citing of the citing document, the cited document, thereason-for-citing (or cited-text-area) of the cited document, and thesimilarity value. At block 156 it is determined whether or not there areremaining documents in the corpus. If yes, the process is repeated atblock 152. If no, the process ends at block 157. In this manner,citation entries regarding semantically-paired documents and passagesfor each document in the corpus may be recorded in the citation-pairingmetadata file.

As stated above, in the common law tradition, cases are normally arguedwith points or issues that are supported by legal precedents. Attorneysuse citations to establish authority of the precedents in support oftheir propositions. In this regard, the citations and legal issuesbehind them form an approximate skeleton of a case. Against thisbackground of normalized issues, such as legal issues discussed within alegal document corpus, the normalized issues may be used as units (i.e.,nodes) within an issue network extracted from the document corpus.

With issues extracted, normalized, and indexed, additional data may becreated underneath the cases data, where each case is represented by theissues it contains. More specifically, metadata may be created thatstores vectors pointing to each issue discussed by individual caseswithin the corpus on a case-by-case basis. Such metadata is referred toherein as issues-by-case metadata. For example, a first case may discussten normalized issues extracted and stored in the issue library. Thecase identifier and the ten normalized issues may be stored in theissue-by-case metadata.

A non-limiting example of issues-by-case entry is provided below:

CaseID:Issue_Indentifier₁; Issue_Identifier₂; Issue_Identifier_(n)

The CaseID may be the same document identifier described above, whereinthe document identifier points to or is otherwise associated with aparticular document in the corpus. The Issue_Indentifier vectors pointto the various normalized issues within the issue library discussed bythe case identified by the CaseID. In this manner, the issues-by-caseentry provides a listing of all of the normalized issues discussed bythe text of the case or other type of document. Below is a non-limitingexample of a sample issues-by-case metadata file in table-format:

TABLE 2 CaseID Issue_Identifier CASE_00000001 LLI_000055; LLI_000321;LLI_990175; . . . CASE_00000002 LLI_000972; LLI_017543; LLI_100095; . .. CASE_00000003 LLI_000055; LLI_000781; LLI_007850; . . . . . . . . .

As shown in the above example, the case having CaseID CASE_(—)000000001discusses as least normalized legal issues LLI_(—)000055; LLI_(—)000321;and LLI_(—)990175, which are stored in the issue library metadata file.Accordingly, one may easily access information regarding all of thenormalized issues discussed by each case in the corpus using theissues-by-case and issue library metadata.

The issues-by-case metadata is an extra-semantic structure that issuperimposed onto the legal data. As described in more detail below, itmay facilitate calculation of distance between cases in a new direction,i.e., based on the issues that they share as evidenced by a network ofissues. This metadata may also provide for more efficient study of legalprinciples (or other principles), how they are used in legal arguments,and what kind of relationships they have among themselves, etc.

The collection of issues extracted from the document corpus may be seenas a condensed version of the knowledge within the corpus. In the legalcontext, each issue may be considered a small portion of the law. Thismay be especially important in legal systems that follows common lawtraditions because substantial areas of the law are not necessarilycodified in the same manner one might find for other continental legalsystems. Thus, for common law systems, the extracted legal issue librarymay serve as a particularly effective vehicle for the study of legalprinciples and their interactions.

Like other semantic units in legal data (e.g., concepts), legal issuesare connected by citations and associated semantic elements. When theyare used as basic operation units to form networks, much of the moreprofound legal knowledge that has not been easily seen may becomeapparent. In embodiments described herein, the relationships betweenextracted and normalized issues are determined and used to form anetwork of issues. In some embodiments, the issues-by-case metadata isdata-mined to determine the co-occurrence of normalized issues withinindividual documents of the document corpus. Accordingly, the issuelibrary metadata and the issues-by-case metadata may be used to generatean issue network that illustrated the connectedness of the variousnormalized issues extracted from the corpus.

One exemplary method of generating an issue network is to evaluate thenormalized issues discussed by the cases. Normalization of the issuesallows issues and related issues discussed within the corpus to bedata-mined. For example, a method may start with a starting issue tolocate all of the cases within the corpus that discusses the startingissue (i.e., a set of cases). As each case within the set of casesdiscusses a plurality of issues, the method may determine some or all ofthe issues discussed by each case that discusses the starting issue(i.e., a set of normalized issues). To create the issue network,co-occurrences of the normalized issues within individual cases may bedetermined by computer processing. The number of issues co-occurringtogether within individual cases indicates the strength of theconnection between the two issues, which act as nodes within the issuenetwork. In some embodiments, only those co-occurrences that appeargreater than or equal to a co-occurrence threshold (e.g., a number oftimes, or within a percentage of the set of documents) are included inthe extracted network or sub-network. In this manner, issues thatco-occur within only a few cases may be excluded.

It should be understood that the issues may be extracted and normalizedusing the processes described above (i.e., using reason-for-citing andissue library metadata) or by other processes. The issues may benormalized in a manner other than those described herein.

As an example, the issues-by-case metadata file may be data-mined tofind the co-occurrences of normalized issues within cases. Referring toTable 2 above, the cases represented by CASE_(—)000000001 andCASE_(—)000000003 each share the legal issue represented byLLI_(—)000055, which points to a particular normalized issue in thelegal issue library, and which, without limitation, may be representedby an entry having a format as described above (e.g., sample text,instances, taxonomy information, etc.). The co-occurrence of normalizedissues may be determined using metadata or information other than theissues-by-case metadata described above. A map may be generated thatlinks the related normalized issues together, wherein the strength ofthe relationship between individual normalized issues may be graphicallydepicted.

Issues-by-case metadata described above was searched using a startingissue relating to the normalized issue “Motivation Element Required forRobbery” (LLI_(—)001) to generate an issue network (or a sub-network ofa larger issue network). It should be understood that the exampledescribed below is for illustrative purposes and that embodiments arenot limited thereto. United States state and federal case law wassearched. About seventy cases were found to discuss the starting issueLLI_(—)001. These seventy cases discussed about 4,000 normalized issuesaccording to the issues-by-case metadata. The following normalizedissues were shown to be related (i.e., co-occur together within cases):

-   -   LLI_(—)001 (Starting Issue): “In order to constitute robbery        rather than theft, the act of force or intimidation must be        motivated by the intent to steal; if the larcenous purpose does        not arise until after the force has been used against the        victim, there is no joint operation of act and intent necessary        to constitute robbery.”    -   LLI_(—)002: “A reviewing court must ‘review the whole record in        the light most favorable to the judgment below to determine        whether it discloses substantial evidence—that is, evidence        which is reasonable, credible, and of solid value—such that a        reasonable trier of fact could find the defendant guilty beyond        a reasonable doubt.”    -   LLI_(—)009: “Prejudice is shown when there is a ‘reasonable        probability that, but for counsel's unprofessional errors, the        result of the proceeding would have been different. A reasonable        probability is a probability sufficient to undermine confidence        in the outcome.”    -   LLI_(—)011: “The quantum of evidence the people must produce in        order to satisfy the corpus delicti rule is quite modest; case        law describes it as a slight or prima facie showing.”    -   LLI_(—)012: “The intentional commission of the underlying felony        is not only an essential element of the crime of first degree        felony murder; it is the sole basis for holding the killing is        murder in the first degree.”    -   LLI_(—)017: “Robbery is defined as the ‘felonious taking of        personal property in the possession of another, from his person        or immediate presence, and against his will, accomplished by        means of force or fear.’”    -   LLI_(—)027: “Conduct by a prosecutor that does not render a        criminal trial fundamentally unfair is prosecutorial misconduct        under California law only if it involves the use of deceptive or        reprehensible methods to attempt to persuade either the court or        the jury.”    -   LLI_(—)048: “The force or fear element of robbery may be        directed either to the initial taking of the property or to its        asportation. Thus, even when the intent to steal arises after        the use of force or fear, the offense is robbery and not theft        if the force or fear was used to escape with the property.”    -   LLI_(—)147: “The trial court has a sua sponte duty to instruct        on lesser included offenses when the evidence raises a question        as to whether all of the elements of the charged offense were        present and there is evidence that would justify a conviction of        such a lesser offense.”    -   LLI_(—)196: “A defendant claiming ineffective assistance of        counsel must first establish that ‘counsels’ representation fell        below an objective standard of reasonableness . . . [P] under        prevailing professional norms.”    -   LLI_(—)213: “The trial court is required to instruct sua sponte        only on general principles of law relevant to issues raised by        the evidence and on particular defenses when a defendant appears        to be relying on such defense and there is substantial evidence        to support it.”    -   LLI_(—)264: “An error in failing to instruct on lesser included        offenses requires reversal unless it can be determined that the        factual question posed by the omitted instruction was        necessarily resolved adversely to the defendant under other,        properly given instructions.”

It should be understood that the issue identifiers provided above areused for illustrative purposes only.

As noted above, these disparate issues form a small sub-network, whichis part of the general legal issue network of United States law, wherenodes (i.e., issues) are linked by edges. FIG. 10 provides a graphicalrepresentation of the network or sub-network of legal issues extractedfrom the corpus as described above. The graphical representation of anextracted network or sub-network may be displayed on a display device,such as a computer monitor. The weight of the edges (i.e., thickness ofthe lines connecting the nodes) provides visual feedback as to thestrength of the connection between connected issues. Accordingly, asshown in the sample issue sub-network or network, not all members of thenetwork play equally strong roles in establishing network cohesion.

Even within a network or sub-network, smaller sub-networks may beidentified. For example, from the network depicted in FIG. 10, the“Definition of Robbery (LLI_(—)017) has a stronger connection to theStarting Issue (LLI_(—)001). It also has a stronger connection to a fewother issues, such as “Review of Evidence” (LLI_(—)002), and “Use ofForce” (LLI_(—)048). The issue “Court's Duty to Instruct on the Lesser”(CL_(—)147) has a stronger connection to “Required Reversal orResolution When Error is Made with that Respect” (LLI_(—)264).

In the legal context, attorneys and judges use legal issues in theirarguments. The selection and use of these issues influences, to a largeextent, the outcome of cases and the development of the common law. Thelegal issue metadata described herein may provide a way to study intothe logical thinking and strategy behind the argument of cases. Legalexperts may also find it useful as to when and how cases share the sameset of issues when formulating their respective argument strategies. Theissue networks described herein may provide legal experts with a tool tofind such cases.

As an example and not a limitation, based on the small network describedabove and illustrated in FIG. 10, two cases showed particularly highoverlap of issue usage (i.e., the discussion of common issues).Specifically, “PEOPLE v. CANTWELL, 2004 Cal. App. Unpub LEXIS 1833” and“People v. Frye, 18 Cal. 4^(th) 894” discussed the following normalizedissues identified from the network:

-   -   LLI_(—)002: “A reviewing court must ‘review the whole record in        the light most favorable to the judgment below to determine        whether it discloses substantial evidence—that is, evidence        which is reasonable, credible, and of solid value—such that a        reasonable trier of fact could find the defendant guilty beyond        a reasonable doubt.”    -   LLI_(—)017: “Robbery is defined as the ‘felonious taking of        personal property in the possession of another, from his person        or immediate presence, and against his will, accomplished by        means of force or fear.’”    -   LLI_(—)027: “Conduct by a prosecutor that does not render a        criminal trial fundamentally unfair is prosecutorial misconduct        under California law only if it involves the use of deceptive or        reprehensible methods to attempt to persuade either the court or        the jury.”    -   LLI_(—)196: “A defendant claiming ineffective assistance of        counsel must first establish that ‘counsels’ representation fell        below an objective standard of reasonableness . . . [P] . . .        under prevailing professional norms.”

Legal experts may find this higher degree of issue-sharing evidenced bythe extracted network as an indication of two cases sharing similarfactual patterns, similar argument strategies, or both.

It should be understood that embodiments described herein are directedto systems and methods of extracting and building of both issuelibraries and issue networks. Such collections may be seen as a summaryor condensed version of knowledge found within the corpus of documents.The issue network(s) may serve as an added semantic layer for thecorpus, and may serve as well as a foundation for differentsemantics-based research tools. The extracted network may providepractitioners with an understanding of how various issues are related,which may assist in the development of strong legal arguments.

While particular embodiments have been illustrated and described herein,it should be understood that various other changes and modifications maybe made without departing from the spirit and scope of the claimedsubject matter. Moreover, although various aspects of the claimedsubject matter have been described herein, such aspects need not beutilized in combination. It is therefore intended that the appendedclaims cover all such changes and modifications that are within thescope of the claimed subject matter.

What is claimed is:
 1. A computer-implemented method of generating anissue network from a document corpus, the method comprising: searching,using a computer, the document corpus for a set of documents discussinga starting issue, wherein the starting issue is one of a plurality ofnormalized issues defined by the document corpus; determining a set ofnormalized issues discussed by the set of documents discussing thestarting issue, wherein the set of normalized issues also includes thestarting issue; determining instances of co-occurrences of individualnormalized issues of the set of normalized issues within individualcases of the set of documents; and linking individual normalized issuesof the set of normalized issues based on their co-occurrences within theset of documents, wherein the linked individual normalized issues atleast in part define the issue network.
 2. The computer-implementedmethod of claim 1, further comprising providing for display a graphicalrepresentation of the issue network on a display device.
 3. Thecomputer-implemented method of claim 2, wherein the graphicalrepresentation of the issue network comprises nodes representingindividual normalized issues of the set of normalized issues, and edgeslinking the nodes based on the co-occurrences of the individualnormalized issues within individual documents within the set ofdocuments.
 4. The computer-implemented method of claim 3, wherein eachedge provides a visual representation of a strength of a link betweentwo nodes based on a number of co-occurrences between two individualissues represented by the two nodes.
 5. The computer-implemented methodof claim 4, wherein the visual representation comprises a weighted linerepresenting the edge.
 6. The computer-implemented method of claim 1,further comprising normalizing issues discussed in the document corpus.7. The computer-implemented method of claim 6, further comprisingstoring normalized issues in an issue library metadata file.
 8. Thecomputer-implemented method of claim 6, wherein normalizing the issuesdiscussed in the document corpus comprises: semantically linking, by acomputing device, documents within the document corpus by pairingreasons-for-citing in citing documents with cited-text-areas in citeddocuments, wherein a cited-text-area in a cited document is a text areathat has a highest similarity value of text present within the citeddocument; creating a group of semantically-similar reasons-for-citingand cited-text-areas that are semantically similar to at least oneissue; and storing information regarding groups of semantically-similarreasons-for-citing and cited-text-areas in an issue library metadataentity, wherein each issue library metadata entity is associated with anindividual issue.
 9. The computer-implemented method of claim 1, furthercomprising creating at least one issues-by-case metadata file, whereinthe searching of the document corpus for the set of documents discussingthe starting issue, the determining of the set of normalized issuesdiscussed by the set of documents discussing the starting issue, and thedetermining of the instances of co-occurrences of individual normalizedissues of the set of normalized issues within individual cases of theset of documents comprises searching the at least one issues-by-casemetadata file.
 10. The computer-implemented method of claim 9, whereinthe at least one issues-by-case metadata file comprises at least oneentry comprising a case identifier and one or more issue identifiers.11. A computer-implemented system for generating an issue network from adocument corpus, wherein documents within the document corpus are linkedby citations, thereby forming a citation network, thecomputer-implemented system comprising a processor and a non-transitorycomputer-readable medium storing computer readable instructions that,when executed by the processor, cause the processor to: search thedocument corpus for a set of documents discussing a starting issue,wherein the starting issue is one of a plurality of normalized issuesfound within the document corpus; determine a set of normalized issuesdiscussed by the set of documents discussing the starting issue, whereinthe set of normalized issues also includes the starting issue; determineco-occurrences of individual normalized issues of the set of normalizedissues within individual cases of the set of documents; and linkindividual normalized issues of the set of normalized issues based ontheir co-occurrences within the set of documents, wherein the linkedindividual normalized issues at least in part define the issue network.12. The computer-implemented system of claim 11, wherein the computerreadable instructions further cause the processor to provide for displaya graphical representation of the issue network on a display device. 13.The computer-implemented system of claim 12, wherein the graphicalrepresentation of the issue network comprises nodes representingindividual normalized issues of the set of normalized issues, and edgeslinking the nodes based on the co-occurrences of the individualnormalized issues within individual documents within the set ofdocuments.
 14. The computer-implemented system of claim 13, wherein thenodes represent individual normalized issues of the set of normalizedissues that co-occur within the individual documents above aco-occurrence threshold.
 15. The computer-implemented system of claim13, wherein each edge provides a visual representation of a strength ofa link between two nodes based on a number of co-occurrences between twoindividual issues represented by the two nodes.
 16. Thecomputer-implemented system of claim 15, wherein the visualrepresentation comprises a weighted line representing the edge.
 17. Thecomputer-implemented system of claim 11, wherein the computer readableinstructions further cause the processor to normalize issues discussedin the document corpus.
 18. The computer-implemented system of claim 17,wherein the computer readable instructions further cause the processorto store normalized issues in an issue library metadata file.
 19. Thecomputer-implemented system of claim 11, wherein the computer readableinstructions further cause the processor to create at least oneissues-by-case metadata file.
 20. The computer-implemented system ofclaim 19, wherein the at least one issues-by-case metadata filecomprises at least one entry comprising a case identifier and one ormore issue identifiers.